Overview

Dataset statistics

Number of variables16
Number of observations199146
Missing cells39721
Missing cells (%)1.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory108.0 MiB
Average record size in memory568.6 B

Variable types

Numeric9
Categorical7

Alerts

FORMULARIO has a high cardinality: 199146 distinct values High cardinality
FECHA_OCURRENCIA_ACC has a high cardinality: 2445 distinct values High cardinality
DIRECCION has a high cardinality: 100085 distinct values High cardinality
FECHA_HORA_ACC has a high cardinality: 162457 distinct values High cardinality
X is highly correlated with LONGITUDHigh correlation
Y is highly correlated with LATITUDHigh correlation
OBJECTID is highly correlated with CODIGO_ACCIDENTE and 1 other fieldsHigh correlation
CODIGO_ACCIDENTE is highly correlated with OBJECTID and 1 other fieldsHigh correlation
ANO_OCURRENCIA_ACC is highly correlated with OBJECTID and 1 other fieldsHigh correlation
LATITUD is highly correlated with YHigh correlation
LONGITUD is highly correlated with XHigh correlation
X is highly correlated with LONGITUDHigh correlation
Y is highly correlated with LATITUDHigh correlation
OBJECTID is highly correlated with CODIGO_ACCIDENTE and 1 other fieldsHigh correlation
CODIGO_ACCIDENTE is highly correlated with OBJECTID and 1 other fieldsHigh correlation
ANO_OCURRENCIA_ACC is highly correlated with OBJECTID and 1 other fieldsHigh correlation
LATITUD is highly correlated with YHigh correlation
LONGITUD is highly correlated with XHigh correlation
X is highly correlated with LONGITUDHigh correlation
Y is highly correlated with LATITUDHigh correlation
CODIGO_ACCIDENTE is highly correlated with ANO_OCURRENCIA_ACCHigh correlation
ANO_OCURRENCIA_ACC is highly correlated with CODIGO_ACCIDENTEHigh correlation
LATITUD is highly correlated with YHigh correlation
LONGITUD is highly correlated with XHigh correlation
X is highly correlated with Y and 4 other fieldsHigh correlation
Y is highly correlated with X and 4 other fieldsHigh correlation
OBJECTID is highly correlated with CODIGO_ACCIDENTE and 1 other fieldsHigh correlation
CODIGO_ACCIDENTE is highly correlated with OBJECTID and 1 other fieldsHigh correlation
ANO_OCURRENCIA_ACC is highly correlated with OBJECTID and 1 other fieldsHigh correlation
LOCALIDAD is highly correlated with X and 4 other fieldsHigh correlation
LATITUD is highly correlated with X and 4 other fieldsHigh correlation
LONGITUD is highly correlated with X and 4 other fieldsHigh correlation
CIV is highly correlated with X and 4 other fieldsHigh correlation
PK_CALZADA has 37974 (19.1%) missing values Missing
FORMULARIO is uniformly distributed Uniform
FECHA_HORA_ACC is uniformly distributed Uniform
OBJECTID has unique values Unique
FORMULARIO has unique values Unique
CODIGO_ACCIDENTE has unique values Unique

Reproduction

Analysis started2022-05-20 01:24:36.781930
Analysis finished2022-05-20 01:25:07.514404
Duration30.73 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

X
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct109453
Distinct (%)55.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-74.10409559
Minimum-74.2283
Maximum-74.011
Zeros0
Zeros (%)0.0%
Negative199146
Negative (%)100.0%
Memory size1.5 MiB
2022-05-20T01:25:07.655657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-74.2283
5-th percentile-74.17267833
Q1-74.13419566
median-74.10304278
Q3-74.07300849
95-th percentile-74.04185926
Maximum-74.011
Range0.2173
Interquartile range (IQR)0.06118717125

Descriptive statistics

Standard deviation0.04009853015
Coefficient of variation (CV)-0.00054111085
Kurtosis-0.6560623801
Mean-74.10409559
Median Absolute Deviation (MAD)0.030506752
Skewness-0.1595573156
Sum-14757534.22
Variance0.00160789212
MonotonicityNot monotonic
2022-05-20T01:25:07.794167image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-74.103254
 
0.1%
-74.138244
 
0.1%
-74.112221
 
0.1%
-74.139216
 
0.1%
-74.15405306206
 
0.1%
-74.079195
 
0.1%
-74.08952915194
 
0.1%
-74.1189
 
0.1%
-74.084184
 
0.1%
-74.11448906176
 
0.1%
Other values (109443)197067
99.0%
ValueCountFrequency (%)
-74.22831
< 0.1%
-74.2181
< 0.1%
-74.21524141
< 0.1%
-74.2152
< 0.1%
-74.214982721
< 0.1%
-74.214950121
< 0.1%
-74.214920741
< 0.1%
-74.214776151
< 0.1%
-74.21471
< 0.1%
-74.21461
< 0.1%
ValueCountFrequency (%)
-74.0111
< 0.1%
-74.0132
< 0.1%
-74.0132471
< 0.1%
-74.01391
< 0.1%
-74.013918741
< 0.1%
-74.013985281
< 0.1%
-74.0141
< 0.1%
-74.014052881
< 0.1%
-74.014478671
< 0.1%
-74.014611621
< 0.1%

Y
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct110417
Distinct (%)55.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.649135264
Minimum4.0858
Maximum4.828040683
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2022-05-20T01:25:07.942252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4.0858
5-th percentile4.559559054
Q14.608163175
median4.645415342
Q34.690015397
95-th percentile4.746763261
Maximum4.828040683
Range0.742240683
Interquartile range (IQR)0.081852222

Descriptive statistics

Standard deviation0.05756745119
Coefficient of variation (CV)0.01238239972
Kurtosis-0.2474953999
Mean4.649135264
Median Absolute Deviation (MAD)0.0412219065
Skewness0.01773892237
Sum925856.6914
Variance0.003314011436
MonotonicityNot monotonic
2022-05-20T01:25:08.076348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.597355
 
0.2%
4.632361373206
 
0.1%
4.695739417194
 
0.1%
4.63186
 
0.1%
4.615541137176
 
0.1%
4.627167
 
0.1%
4.627151357155
 
0.1%
4.751609833155
 
0.1%
4.628154
 
0.1%
4.629153
 
0.1%
Other values (110407)197245
99.0%
ValueCountFrequency (%)
4.08581
< 0.1%
4.1912484081
< 0.1%
4.30311
< 0.1%
4.3721
< 0.1%
4.3821
< 0.1%
4.3851
< 0.1%
4.3861
< 0.1%
4.3871
< 0.1%
4.3882448671
< 0.1%
4.3911
< 0.1%
ValueCountFrequency (%)
4.8280406831
 
< 0.1%
4.8252
 
< 0.1%
4.824429321
 
< 0.1%
4.8237521661
 
< 0.1%
4.8231545921
 
< 0.1%
4.8219244731
 
< 0.1%
4.8211
 
< 0.1%
4.82081
 
< 0.1%
4.82076241712
< 0.1%
4.8206948771
 
< 0.1%

OBJECTID
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct199146
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean246116.2682
Minimum1
Maximum421911
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2022-05-20T01:25:08.230926image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile17822.5
Q1135603.75
median283027.5
Q3358176.75
95-th percentile411953.75
Maximum421911
Range421910
Interquartile range (IQR)222573

Descriptive statistics

Standard deviation128297.6317
Coefficient of variation (CV)0.5212887092
Kurtosis-1.136518307
Mean246116.2682
Median Absolute Deviation (MAD)93717
Skewness-0.4386851747
Sum4.901307034 × 1010
Variance1.646028231 × 1010
MonotonicityStrictly increasing
2022-05-20T01:25:08.368609image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
< 0.1%
3411131
 
< 0.1%
3410911
 
< 0.1%
3410921
 
< 0.1%
3410931
 
< 0.1%
3410941
 
< 0.1%
3410951
 
< 0.1%
3410961
 
< 0.1%
3410971
 
< 0.1%
3410981
 
< 0.1%
Other values (199136)199136
> 99.9%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
41
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
121
< 0.1%
131
< 0.1%
151
< 0.1%
ValueCountFrequency (%)
4219111
< 0.1%
4219101
< 0.1%
4219091
< 0.1%
4219081
< 0.1%
4219071
< 0.1%
4219061
< 0.1%
4219051
< 0.1%
4219041
< 0.1%
4219031
< 0.1%
4219021
< 0.1%

FORMULARIO
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct199146
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size12.6 MiB
A000640275
 
1
A001057677
 
1
A000872644
 
1
A001303945
 
1
A001299396
 
1
Other values (199141)
199141 

Length

Max length11
Median length10
Mean length9.560382835
Min length2

Characters and Unicode

Total characters1903912
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique199146 ?
Unique (%)100.0%

Sample

1st rowA000640275
2nd rowA001233353
3rd rowA001232786
4th rowA000200705
5th rowA000402862

Common Values

ValueCountFrequency (%)
A0006402751
 
< 0.1%
A0010576771
 
< 0.1%
A0008726441
 
< 0.1%
A0013039451
 
< 0.1%
A0012993961
 
< 0.1%
A0012979681
 
< 0.1%
A0012383871
 
< 0.1%
A0010580341
 
< 0.1%
A0010579791
 
< 0.1%
A0010585151
 
< 0.1%
Other values (199136)199136
> 99.9%

Length

2022-05-20T01:25:08.643357image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a0006402751
 
< 0.1%
a0008161581
 
< 0.1%
a0006904681
 
< 0.1%
a0012350381
 
< 0.1%
a0011855261
 
< 0.1%
a0011839321
 
< 0.1%
a0011803021
 
< 0.1%
a0012327861
 
< 0.1%
a0002007051
 
< 0.1%
a0004028621
 
< 0.1%
Other values (199136)199136
> 99.9%

Most occurring characters

ValueCountFrequency (%)
0612727
32.2%
A199146
 
10.5%
1181332
 
9.5%
6123831
 
6.5%
3119791
 
6.3%
4117287
 
6.2%
2115104
 
6.0%
9112019
 
5.9%
7111080
 
5.8%
8108141
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1704766
89.5%
Uppercase Letter199146
 
10.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0612727
35.9%
1181332
 
10.6%
6123831
 
7.3%
3119791
 
7.0%
4117287
 
6.9%
2115104
 
6.8%
9112019
 
6.6%
7111080
 
6.5%
8108141
 
6.3%
5103454
 
6.1%
Uppercase Letter
ValueCountFrequency (%)
A199146
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1704766
89.5%
Latin199146
 
10.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0612727
35.9%
1181332
 
10.6%
6123831
 
7.3%
3119791
 
7.0%
4117287
 
6.9%
2115104
 
6.8%
9112019
 
6.6%
7111080
 
6.5%
8108141
 
6.3%
5103454
 
6.1%
Latin
ValueCountFrequency (%)
A199146
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1903912
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0612727
32.2%
A199146
 
10.5%
1181332
 
9.5%
6123831
 
6.5%
3119791
 
6.3%
4117287
 
6.2%
2115104
 
6.0%
9112019
 
5.9%
7111080
 
5.8%
8108141
 
5.7%

CODIGO_ACCIDENTE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct199146
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7368237.845
Minimum4401420
Maximum10549255
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2022-05-20T01:25:08.760762image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4401420
5-th percentile4412974.25
Q14458053.25
median4512847.5
Q310497938.5
95-th percentile10539216.75
Maximum10549255
Range6147835
Interquartile range (IQR)6039885.25

Descriptive statistics

Standard deviation3017820.345
Coefficient of variation (CV)0.409571516
Kurtosis-1.994164264
Mean7368237.845
Median Absolute Deviation (MAD)107110
Skewness0.0736155348
Sum1.467355094 × 1012
Variance9.107239632 × 1012
MonotonicityNot monotonic
2022-05-20T01:25:08.893823image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44846601
 
< 0.1%
105014751
 
< 0.1%
104622451
 
< 0.1%
105407281
 
< 0.1%
105398601
 
< 0.1%
105383521
 
< 0.1%
105357161
 
< 0.1%
105014341
 
< 0.1%
105014351
 
< 0.1%
105014381
 
< 0.1%
Other values (199136)199136
> 99.9%
ValueCountFrequency (%)
44014201
< 0.1%
44014211
< 0.1%
44014221
< 0.1%
44014231
< 0.1%
44014241
< 0.1%
44014251
< 0.1%
44014261
< 0.1%
44014281
< 0.1%
44014291
< 0.1%
44014301
< 0.1%
ValueCountFrequency (%)
105492551
< 0.1%
105492541
< 0.1%
105492531
< 0.1%
105492521
< 0.1%
105492511
< 0.1%
105492501
< 0.1%
105492491
< 0.1%
105492481
< 0.1%
105492471
< 0.1%
105492461
< 0.1%

FECHA_OCURRENCIA_ACC
Categorical

HIGH CARDINALITY

Distinct2445
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size15.0 MiB
2019/12/06 00:00:00+00
 
155
2016/11/08 00:00:00+00
 
147
2017/10/27 00:00:00+00
 
143
2018/05/11 00:00:00+00
 
141
2018/06/08 00:00:00+00
 
141
Other values (2440)
198419 

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters4381212
Distinct characters14
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017/06/12 00:00:00+00
2nd row2020/11/19 00:00:00+00
3rd row2020/11/10 00:00:00+00
4th row2015/05/11 00:00:00+00
5th row2016/06/08 00:00:00+00

Common Values

ValueCountFrequency (%)
2019/12/06 00:00:00+00155
 
0.1%
2016/11/08 00:00:00+00147
 
0.1%
2017/10/27 00:00:00+00143
 
0.1%
2018/05/11 00:00:00+00141
 
0.1%
2018/06/08 00:00:00+00141
 
0.1%
2016/11/15 00:00:00+00139
 
0.1%
2016/10/13 00:00:00+00137
 
0.1%
2018/03/09 00:00:00+00137
 
0.1%
2019/11/30 00:00:00+00136
 
0.1%
2020/02/01 00:00:00+00134
 
0.1%
Other values (2435)197736
99.3%

Length

2022-05-20T01:25:09.025139image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:00+00199146
50.0%
2019/12/06155
 
< 0.1%
2016/11/08147
 
< 0.1%
2017/10/27143
 
< 0.1%
2018/05/11141
 
< 0.1%
2018/06/08141
 
< 0.1%
2016/11/15139
 
< 0.1%
2016/10/13137
 
< 0.1%
2018/03/09137
 
< 0.1%
2019/11/30136
 
< 0.1%
Other values (2436)197870
49.7%

Most occurring characters

ValueCountFrequency (%)
02059388
47.0%
/398292
 
9.1%
:398292
 
9.1%
2357280
 
8.2%
1344233
 
7.9%
199146
 
4.5%
+199146
 
4.5%
871091
 
1.6%
769630
 
1.6%
968920
 
1.6%
Other values (4)215794
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3186336
72.7%
Other Punctuation796584
 
18.2%
Space Separator199146
 
4.5%
Math Symbol199146
 
4.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02059388
64.6%
2357280
 
11.2%
1344233
 
10.8%
871091
 
2.2%
769630
 
2.2%
968920
 
2.2%
668422
 
2.1%
564749
 
2.0%
347749
 
1.5%
434874
 
1.1%
Other Punctuation
ValueCountFrequency (%)
/398292
50.0%
:398292
50.0%
Space Separator
ValueCountFrequency (%)
199146
100.0%
Math Symbol
ValueCountFrequency (%)
+199146
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4381212
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02059388
47.0%
/398292
 
9.1%
:398292
 
9.1%
2357280
 
8.2%
1344233
 
7.9%
199146
 
4.5%
+199146
 
4.5%
871091
 
1.6%
769630
 
1.6%
968920
 
1.6%
Other values (4)215794
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII4381212
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02059388
47.0%
/398292
 
9.1%
:398292
 
9.1%
2357280
 
8.2%
1344233
 
7.9%
199146
 
4.5%
+199146
 
4.5%
871091
 
1.6%
769630
 
1.6%
968920
 
1.6%
Other values (4)215794
 
4.9%

ANO_OCURRENCIA_ACC
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017.760106
Minimum2015
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2022-05-20T01:25:09.120832image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2015
5-th percentile2015
Q12016
median2018
Q32019
95-th percentile2021
Maximum2021
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.855070873
Coefficient of variation (CV)0.00091937137
Kurtosis-1.060527733
Mean2017.760106
Median Absolute Deviation (MAD)2
Skewness0.1232263737
Sum401828854
Variance3.441287943
MonotonicityNot monotonic
2022-05-20T01:25:09.217942image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
201833418
16.8%
201932962
16.6%
201732415
16.3%
201631928
16.0%
201527885
14.0%
202022424
11.3%
202118114
9.1%
ValueCountFrequency (%)
201527885
14.0%
201631928
16.0%
201732415
16.3%
201833418
16.8%
201932962
16.6%
202022424
11.3%
202118114
9.1%
ValueCountFrequency (%)
202118114
9.1%
202022424
11.3%
201932962
16.6%
201833418
16.8%
201732415
16.3%
201631928
16.0%
201527885
14.0%

DIRECCION
Categorical

HIGH CARDINALITY

Distinct100085
Distinct (%)50.3%
Missing0
Missing (%)0.0%
Memory size14.3 MiB
KR 80-CL 2 51
 
213
AV AVENIDA BOYACA-CL 80 02
 
153
CL 13-KR 72 02
 
149
CL 100-KR 15 02
 
144
AV AVENIDA CIUDAD DE CALI-CL 26 02
 
140
Other values (100080)
198347 

Length

Max length73
Median length69
Mean length18.10139797
Min length11

Characters and Unicode

Total characters3604821
Distinct characters45
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique73029 ?
Unique (%)36.7%

Sample

1st rowAV AVENIDA BOYACA-CL 79 02
2nd rowCL 26 S- KR 50 02
3rd rowKR 9 - CL 100 02
4th rowCL 63A-KR 72 S 02
5th rowKR 27-CL 9 14

Common Values

ValueCountFrequency (%)
KR 80-CL 2 51213
 
0.1%
AV AVENIDA BOYACA-CL 80 02153
 
0.1%
CL 13-KR 72 02149
 
0.1%
CL 100-KR 15 02144
 
0.1%
AV AVENIDA CIUDAD DE CALI-CL 26 02140
 
0.1%
AV AVENIDA BOYACA-CL 26 02138
 
0.1%
CL 80-KR 72 02135
 
0.1%
KR 50-CL 3 02132
 
0.1%
AV AVENIDA DEL SUR-CL 59 S 02124
 
0.1%
AV AVENIDA DE LAS AMERICAS-KR 68 02118
 
0.1%
Other values (100075)197700
99.3%

Length

2022-05-20T01:25:09.363448image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
02114483
 
12.0%
cl80693
 
8.4%
kr78958
 
8.3%
251145
 
5.3%
s46375
 
4.8%
avenida34828
 
3.6%
av33863
 
3.5%
15546
 
1.6%
de14621
 
1.5%
ak9188
 
1.0%
Other values (6162)476973
49.9%

Most occurring characters

ValueCountFrequency (%)
773557
21.5%
2254937
 
7.1%
C236570
 
6.6%
A226401
 
6.3%
-199142
 
5.5%
L193752
 
5.4%
R185472
 
5.1%
0183022
 
5.1%
K169706
 
4.7%
1145763
 
4.0%
Other values (35)1036499
28.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1541642
42.8%
Decimal Number1090368
30.2%
Space Separator773557
21.5%
Dash Punctuation199142
 
5.5%
Lowercase Letter112
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C236570
15.3%
A226401
14.7%
L193752
12.6%
R185472
12.0%
K169706
11.0%
D79985
 
5.2%
V74898
 
4.9%
S71092
 
4.6%
I68661
 
4.5%
E67623
 
4.4%
Other values (16)167482
10.9%
Decimal Number
ValueCountFrequency (%)
2254937
23.4%
0183022
16.8%
1145763
13.4%
683334
 
7.6%
781668
 
7.5%
378141
 
7.2%
572200
 
6.6%
869284
 
6.4%
468782
 
6.3%
953237
 
4.9%
Lowercase Letter
ValueCountFrequency (%)
l44
39.3%
n22
19.6%
u22
19.6%
a9
 
8.0%
c7
 
6.2%
b6
 
5.4%
f2
 
1.8%
Space Separator
ValueCountFrequency (%)
773557
100.0%
Dash Punctuation
ValueCountFrequency (%)
-199142
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2063067
57.2%
Latin1541754
42.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
C236570
15.3%
A226401
14.7%
L193752
12.6%
R185472
12.0%
K169706
11.0%
D79985
 
5.2%
V74898
 
4.9%
S71092
 
4.6%
I68661
 
4.5%
E67623
 
4.4%
Other values (23)167594
10.9%
Common
ValueCountFrequency (%)
773557
37.5%
2254937
 
12.4%
-199142
 
9.7%
0183022
 
8.9%
1145763
 
7.1%
683334
 
4.0%
781668
 
4.0%
378141
 
3.8%
572200
 
3.5%
869284
 
3.4%
Other values (2)122019
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII3604821
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
773557
21.5%
2254937
 
7.1%
C236570
 
6.6%
A226401
 
6.3%
-199142
 
5.5%
L193752
 
5.4%
R185472
 
5.1%
0183022
 
5.1%
K169706
 
4.7%
1145763
 
4.0%
Other values (35)1036499
28.8%

GRAVEDAD
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.8 MiB
SOLO DANOS
128207 
CON HERIDOS
67700 
CON MUERTOS
 
3239

Length

Max length11
Median length10
Mean length10.35621604
Min length10

Characters and Unicode

Total characters2062399
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSOLO DANOS
2nd rowCON HERIDOS
3rd rowSOLO DANOS
4th rowSOLO DANOS
5th rowSOLO DANOS

Common Values

ValueCountFrequency (%)
SOLO DANOS128207
64.4%
CON HERIDOS67700
34.0%
CON MUERTOS3239
 
1.6%

Length

2022-05-20T01:25:09.493608image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-20T01:25:09.610632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
solo128207
32.2%
danos128207
32.2%
con70939
17.8%
heridos67700
17.0%
muertos3239
 
0.8%

Most occurring characters

ValueCountFrequency (%)
O526499
25.5%
S327353
15.9%
199146
 
9.7%
N199146
 
9.7%
D195907
 
9.5%
L128207
 
6.2%
A128207
 
6.2%
C70939
 
3.4%
E70939
 
3.4%
R70939
 
3.4%
Other values (5)145117
 
7.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1863253
90.3%
Space Separator199146
 
9.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O526499
28.3%
S327353
17.6%
N199146
 
10.7%
D195907
 
10.5%
L128207
 
6.9%
A128207
 
6.9%
C70939
 
3.8%
E70939
 
3.8%
R70939
 
3.8%
H67700
 
3.6%
Other values (4)77417
 
4.2%
Space Separator
ValueCountFrequency (%)
199146
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1863253
90.3%
Common199146
 
9.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
O526499
28.3%
S327353
17.6%
N199146
 
10.7%
D195907
 
10.5%
L128207
 
6.9%
A128207
 
6.9%
C70939
 
3.8%
E70939
 
3.8%
R70939
 
3.8%
H67700
 
3.6%
Other values (4)77417
 
4.2%
Common
ValueCountFrequency (%)
199146
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2062399
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O526499
25.5%
S327353
15.9%
199146
 
9.7%
N199146
 
9.7%
D195907
 
9.5%
L128207
 
6.2%
A128207
 
6.2%
C70939
 
3.4%
E70939
 
3.4%
R70939
 
3.4%
Other values (5)145117
 
7.0%

CLASE_ACC
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.1 MiB
CHOQUE
170802 
ATROPELLO
20138 
CAIDA DE OCUPANTE
 
4639
VOLCAMIENTO
 
2729
OTRO
 
804
Other values (2)
 
34

Length

Max length17
Median length6
Mean length6.62048949
Min length4

Characters and Unicode

Total characters1318444
Distinct characters18
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCHOQUE
2nd rowOTRO
3rd rowCHOQUE
4th rowCHOQUE
5th rowCHOQUE

Common Values

ValueCountFrequency (%)
CHOQUE170802
85.8%
ATROPELLO20138
 
10.1%
CAIDA DE OCUPANTE4639
 
2.3%
VOLCAMIENTO2729
 
1.4%
OTRO804
 
0.4%
INCENDIO24
 
< 0.1%
AUTOLESION10
 
< 0.1%

Length

2022-05-20T01:25:09.716783image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-20T01:25:09.844180image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
choque170802
81.9%
atropello20138
 
9.7%
caida4639
 
2.2%
de4639
 
2.2%
ocupante4639
 
2.2%
volcamiento2729
 
1.3%
otro804
 
0.4%
incendio24
 
< 0.1%
autolesion10
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
O222827
16.9%
E202981
15.4%
C182833
13.9%
U175451
13.3%
H170802
13.0%
Q170802
13.0%
L43015
 
3.3%
A36794
 
2.8%
T28320
 
2.1%
P24777
 
1.9%
Other values (8)59842
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1309166
99.3%
Space Separator9278
 
0.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O222827
17.0%
E202981
15.5%
C182833
14.0%
U175451
13.4%
H170802
13.0%
Q170802
13.0%
L43015
 
3.3%
A36794
 
2.8%
T28320
 
2.2%
P24777
 
1.9%
Other values (7)50564
 
3.9%
Space Separator
ValueCountFrequency (%)
9278
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1309166
99.3%
Common9278
 
0.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
O222827
17.0%
E202981
15.5%
C182833
14.0%
U175451
13.4%
H170802
13.0%
Q170802
13.0%
L43015
 
3.3%
A36794
 
2.8%
T28320
 
2.2%
P24777
 
1.9%
Other values (7)50564
 
3.9%
Common
ValueCountFrequency (%)
9278
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1318444
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O222827
16.9%
E202981
15.4%
C182833
13.9%
U175451
13.3%
H170802
13.0%
Q170802
13.0%
L43015
 
3.3%
A36794
 
2.8%
T28320
 
2.1%
P24777
 
1.9%
Other values (8)59842
 
4.5%

LOCALIDAD
Categorical

HIGH CORRELATION

Distinct20
Distinct (%)< 0.1%
Missing46
Missing (%)< 0.1%
Memory size12.5 MiB
KENNEDY
23661 
ENGATIVA
20928 
USAQUEN
19292 
SUBA
18973 
FONTIBON
16377 
Other values (15)
99869 

Length

Max length18
Median length13
Mean length8.947840281
Min length4

Characters and Unicode

Total characters1781515
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowENGATIVA
2nd rowPUENTE ARANDA
3rd rowUSAQUEN
4th rowCIUDAD BOLIVAR
5th rowLOS MARTIRES

Common Values

ValueCountFrequency (%)
KENNEDY23661
11.9%
ENGATIVA20928
10.5%
USAQUEN19292
9.7%
SUBA18973
9.5%
FONTIBON16377
 
8.2%
PUENTE ARANDA14143
 
7.1%
CHAPINERO11696
 
5.9%
TEUSAQUILLO10167
 
5.1%
BARRIOS UNIDOS10094
 
5.1%
BOSA9417
 
4.7%
Other values (10)44352
22.3%

Length

2022-05-20T01:25:09.964654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kennedy23661
 
9.0%
engativa20928
 
8.0%
usaquen19292
 
7.3%
suba18973
 
7.2%
fontibon16377
 
6.2%
puente14143
 
5.4%
aranda14143
 
5.4%
chapinero11696
 
4.5%
uribe10666
 
4.1%
teusaquillo10167
 
3.9%
Other values (18)102719
39.1%

Most occurring characters

ValueCountFrequency (%)
A228524
12.8%
N202275
11.4%
E175641
9.9%
U135525
 
7.6%
I131292
 
7.4%
O120326
 
6.8%
S110682
 
6.2%
T93074
 
5.2%
R92469
 
5.2%
B78867
 
4.4%
Other values (15)412840
23.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1717850
96.4%
Space Separator63665
 
3.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A228524
13.3%
N202275
11.8%
E175641
10.2%
U135525
 
7.9%
I131292
 
7.6%
O120326
 
7.0%
S110682
 
6.4%
T93074
 
5.4%
R92469
 
5.4%
B78867
 
4.6%
Other values (14)349175
20.3%
Space Separator
ValueCountFrequency (%)
63665
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1717850
96.4%
Common63665
 
3.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
A228524
13.3%
N202275
11.8%
E175641
10.2%
U135525
 
7.9%
I131292
 
7.6%
O120326
 
7.0%
S110682
 
6.4%
T93074
 
5.4%
R92469
 
5.4%
B78867
 
4.6%
Other values (14)349175
20.3%
Common
ValueCountFrequency (%)
63665
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1781515
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A228524
12.8%
N202275
11.4%
E175641
9.9%
U135525
 
7.6%
I131292
 
7.4%
O120326
 
6.8%
S110682
 
6.2%
T93074
 
5.2%
R92469
 
5.2%
B78867
 
4.4%
Other values (15)412840
23.2%

FECHA_HORA_ACC
Categorical

HIGH CARDINALITY
UNIFORM

Distinct162457
Distinct (%)81.6%
Missing0
Missing (%)0.0%
Memory size15.0 MiB
2018/01/29 13:00:00+00
 
7
2016/12/03 14:00:00+00
 
7
2016/03/08 12:30:00+00
 
7
2016/09/13 15:00:00+00
 
7
2016/06/07 07:00:00+00
 
6
Other values (162452)
199112 

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters4381212
Distinct characters14
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique133857 ?
Unique (%)67.2%

Sample

1st row2017/06/12 05:30:00+00
2nd row2020/11/19 02:05:00+00
3rd row2020/11/10 13:30:00+00
4th row2015/05/11 10:50:00+00
5th row2016/06/08 21:30:00+00

Common Values

ValueCountFrequency (%)
2018/01/29 13:00:00+007
 
< 0.1%
2016/12/03 14:00:00+007
 
< 0.1%
2016/03/08 12:30:00+007
 
< 0.1%
2016/09/13 15:00:00+007
 
< 0.1%
2016/06/07 07:00:00+006
 
< 0.1%
2017/02/17 15:00:00+006
 
< 0.1%
2016/11/21 11:00:00+006
 
< 0.1%
2018/03/21 14:00:00+006
 
< 0.1%
2017/07/15 14:00:00+006
 
< 0.1%
2016/10/12 14:00:00+006
 
< 0.1%
Other values (162447)199082
> 99.9%

Length

2022-05-20T01:25:10.081438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
14:00:00+002487
 
0.6%
13:00:00+002410
 
0.6%
15:00:00+002375
 
0.6%
14:30:00+002346
 
0.6%
13:30:00+002269
 
0.6%
11:00:00+002261
 
0.6%
12:30:00+002259
 
0.6%
16:00:00+002220
 
0.6%
15:30:00+002194
 
0.6%
08:00:00+002186
 
0.5%
Other values (3874)375285
94.2%

Most occurring characters

ValueCountFrequency (%)
01524738
34.8%
1513274
 
11.7%
2429592
 
9.8%
/398292
 
9.1%
:398292
 
9.1%
199146
 
4.5%
+199146
 
4.5%
5139411
 
3.2%
3114185
 
2.6%
896801
 
2.2%
Other values (4)368335
 
8.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3186336
72.7%
Other Punctuation796584
 
18.2%
Space Separator199146
 
4.5%
Math Symbol199146
 
4.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01524738
47.9%
1513274
 
16.1%
2429592
 
13.5%
5139411
 
4.4%
3114185
 
3.6%
896801
 
3.0%
796422
 
3.0%
693478
 
2.9%
993162
 
2.9%
485273
 
2.7%
Other Punctuation
ValueCountFrequency (%)
/398292
50.0%
:398292
50.0%
Space Separator
ValueCountFrequency (%)
199146
100.0%
Math Symbol
ValueCountFrequency (%)
+199146
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4381212
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01524738
34.8%
1513274
 
11.7%
2429592
 
9.8%
/398292
 
9.1%
:398292
 
9.1%
199146
 
4.5%
+199146
 
4.5%
5139411
 
3.2%
3114185
 
2.6%
896801
 
2.2%
Other values (4)368335
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII4381212
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01524738
34.8%
1513274
 
11.7%
2429592
 
9.8%
/398292
 
9.1%
:398292
 
9.1%
199146
 
4.5%
+199146
 
4.5%
5139411
 
3.2%
3114185
 
2.6%
896801
 
2.2%
Other values (4)368335
 
8.4%

LATITUD
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct108427
Distinct (%)54.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.649135262
Minimum4.0858
Maximum4.82804068
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2022-05-20T01:25:10.206644image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4.0858
5-th percentile4.55955905
Q14.60816318
median4.64541534
Q34.6900154
95-th percentile4.746763258
Maximum4.82804068
Range0.74224068
Interquartile range (IQR)0.08185222

Descriptive statistics

Standard deviation0.05756745196
Coefficient of variation (CV)0.01238239989
Kurtosis-0.2474955365
Mean4.649135262
Median Absolute Deviation (MAD)0.041221905
Skewness0.01773905907
Sum925856.6909
Variance0.003314011525
MonotonicityNot monotonic
2022-05-20T01:25:10.354211image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.597355
 
0.2%
4.63236137211
 
0.1%
4.69573942200
 
0.1%
4.63186
 
0.1%
4.61554114179
 
0.1%
4.627167
 
0.1%
4.75160983167
 
0.1%
4.631166
 
0.1%
4.62715136160
 
0.1%
4.628154
 
0.1%
Other values (108417)197201
99.0%
ValueCountFrequency (%)
4.08581
< 0.1%
4.191248411
< 0.1%
4.30311
< 0.1%
4.3721
< 0.1%
4.3821
< 0.1%
4.3851
< 0.1%
4.3861
< 0.1%
4.3871
< 0.1%
4.388244871
< 0.1%
4.3911
< 0.1%
ValueCountFrequency (%)
4.828040681
 
< 0.1%
4.8252
 
< 0.1%
4.824429321
 
< 0.1%
4.823752171
 
< 0.1%
4.823154591
 
< 0.1%
4.821924471
 
< 0.1%
4.8211
 
< 0.1%
4.82081
 
< 0.1%
4.8207624212
< 0.1%
4.820694881
 
< 0.1%

LONGITUD
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct107698
Distinct (%)54.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-74.10409555
Minimum-74.2283
Maximum-74.011
Zeros0
Zeros (%)0.0%
Negative199146
Negative (%)100.0%
Memory size1.5 MiB
2022-05-20T01:25:10.504175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-74.2283
5-th percentile-74.17267833
Q1-74.13419566
median-74.10304278
Q3-74.07300744
95-th percentile-74.04185926
Maximum-74.011
Range0.2173
Interquartile range (IQR)0.06118822

Descriptive statistics

Standard deviation0.04009855411
Coefficient of variation (CV)-0.0005411111736
Kurtosis-0.6560662603
Mean-74.10409555
Median Absolute Deviation (MAD)0.03050675
Skewness-0.1595583456
Sum-14757534.21
Variance0.001607894042
MonotonicityNot monotonic
2022-05-20T01:25:10.644462image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-74.103254
 
0.1%
-74.138244
 
0.1%
-74.112221
 
0.1%
-74.139216
 
0.1%
-74.15405306211
 
0.1%
-74.08952915200
 
0.1%
-74.079195
 
0.1%
-74.1189
 
0.1%
-74.084184
 
0.1%
-74.11448906179
 
0.1%
Other values (107688)197053
98.9%
ValueCountFrequency (%)
-74.22831
< 0.1%
-74.2181
< 0.1%
-74.21524141
< 0.1%
-74.2152
< 0.1%
-74.214982721
< 0.1%
-74.214950121
< 0.1%
-74.214920741
< 0.1%
-74.214776151
< 0.1%
-74.21471
< 0.1%
-74.21461
< 0.1%
ValueCountFrequency (%)
-74.0111
< 0.1%
-74.0132
< 0.1%
-74.0132471
< 0.1%
-74.01391
< 0.1%
-74.013918741
< 0.1%
-74.013985281
< 0.1%
-74.0141
< 0.1%
-74.014052881
< 0.1%
-74.014478671
< 0.1%
-74.014611621
< 0.1%

CIV
Real number (ℝ≥0)

HIGH CORRELATION

Distinct38419
Distinct (%)19.5%
Missing1701
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean13798945.23
Minimum0
Maximum50009618
Zeros883
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2022-05-20T01:25:10.790809image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1004508
Q17004943
median10007581
Q315001163
95-th percentile50007085
Maximum50009618
Range50009618
Interquartile range (IQR)7996220

Descriptive statistics

Standard deviation13467556.24
Coefficient of variation (CV)0.9759844695
Kurtosis2.637417592
Mean13798945.23
Median Absolute Deviation (MAD)3994125
Skewness1.897446863
Sum2.724532741 × 1012
Variance1.813750711 × 1014
MonotonicityNot monotonic
2022-05-20T01:25:10.934708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0883
 
0.4%
10006560324
 
0.2%
8004259318
 
0.2%
13002027308
 
0.2%
9003941287
 
0.1%
10003679249
 
0.1%
10000256229
 
0.1%
8000071228
 
0.1%
10008661226
 
0.1%
8012646220
 
0.1%
Other values (38409)194173
97.5%
(Missing)1701
 
0.9%
ValueCountFrequency (%)
0883
0.4%
10000019
 
< 0.1%
100000220
 
< 0.1%
100000318
 
< 0.1%
10000077
 
< 0.1%
10000092
 
< 0.1%
10000114
 
< 0.1%
10000124
 
< 0.1%
10000161
 
< 0.1%
10000212
 
< 0.1%
ValueCountFrequency (%)
500096181
 
< 0.1%
500096001
 
< 0.1%
500095983
 
< 0.1%
500095682
 
< 0.1%
500095544
< 0.1%
500095511
 
< 0.1%
500095501
 
< 0.1%
500095478
< 0.1%
500095261
 
< 0.1%
500094973
 
< 0.1%

PK_CALZADA
Real number (ℝ≥0)

MISSING

Distinct37953
Distinct (%)23.5%
Missing37974
Missing (%)19.1%
Infinite0
Infinite (%)0.0%
Mean7588678.331
Minimum0
Maximum91030491
Zeros47
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2022-05-20T01:25:11.216472image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4384
Q143267
median176900
Q3235485
95-th percentile50015428
Maximum91030491
Range91030491
Interquartile range (IQR)192218

Descriptive statistics

Standard deviation18408766.92
Coefficient of variation (CV)2.425819901
Kurtosis3.648846114
Mean7588678.331
Median Absolute Deviation (MAD)87282
Skewness2.224388789
Sum1.223082464 × 1012
Variance3.388826994 × 1014
MonotonicityNot monotonic
2022-05-20T01:25:11.356394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32570
 
1.3%
1571
 
0.3%
170406310
 
0.2%
221312210
 
0.1%
50016858184
 
0.1%
193781181
 
0.1%
29830170
 
0.1%
50012516168
 
0.1%
140172156
 
0.1%
34687153
 
0.1%
Other values (37943)156499
78.6%
(Missing)37974
 
19.1%
ValueCountFrequency (%)
047
 
< 0.1%
1571
 
0.3%
32570
1.3%
251
 
< 0.1%
373
 
< 0.1%
381
 
< 0.1%
501
 
< 0.1%
651
 
< 0.1%
701
 
< 0.1%
712
 
< 0.1%
ValueCountFrequency (%)
910304911
 
< 0.1%
910304423
< 0.1%
910301691
 
< 0.1%
910301661
 
< 0.1%
910291181
 
< 0.1%
910291051
 
< 0.1%
910290951
 
< 0.1%
910290931
 
< 0.1%
910290661
 
< 0.1%
910290591
 
< 0.1%

Interactions

2022-05-20T01:25:03.474254image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:24:52.377585image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:24:53.765618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:24:55.245974image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:24:56.451617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:24:57.706517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:24:59.280447image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:25:00.733373image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:25:02.035983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:25:03.629050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:24:52.525773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:24:53.908871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:24:55.385538image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:24:56.588118image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:24:57.840832image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:24:59.507396image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:25:00.878513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:25:02.185487image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:25:03.776892image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:24:52.665678image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:24:54.057662image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:24:55.520067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:24:56.743744image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:24:57.985457image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:24:59.708138image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:25:01.039317image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:25:02.333668image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:25:03.910138image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:24:52.809806image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:24:54.202312image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:24:55.645611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:24:56.872705image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:24:58.117365image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:24:59.850679image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:25:01.167991image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:25:02.473145image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:25:04.047594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:24:52.949335image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:24:54.346042image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:24:55.785063image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:24:57.014724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:24:58.264567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:24:59.996011image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:25:01.313664image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:25:02.620275image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:25:04.192744image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:24:53.089748image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:24:54.501361image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:24:55.923974image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:24:57.163647image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:24:58.412488image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:25:00.147667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:25:01.455718image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:25:02.765317image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:25:04.334583image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:24:53.232719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:24:54.652910image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:24:56.075639image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:24:57.305338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:24:58.709465image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:25:00.304524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:25:01.608377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:25:02.913184image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:25:04.477953image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:24:53.366999image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:24:54.803137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:24:56.202076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:24:57.438549image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:24:58.855455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:25:00.455363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:25:01.744363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:25:03.079368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:25:04.603985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:24:53.530911image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:24:54.955977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:24:56.325255image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:24:57.567602image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:24:59.003564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:25:00.588943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:25:01.888213image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-20T01:25:03.211334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-05-20T01:25:11.486403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-20T01:25:11.632035image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-20T01:25:11.774689image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-20T01:25:11.907112image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-05-20T01:25:12.021330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-20T01:25:05.304242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-20T01:25:05.996897image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-05-20T01:25:06.722444image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-05-20T01:25:07.142181image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

XYOBJECTIDFORMULARIOCODIGO_ACCIDENTEFECHA_OCURRENCIA_ACCANO_OCURRENCIA_ACCDIRECCIONGRAVEDADCLASE_ACCLOCALIDADFECHA_HORA_ACCLATITUDLONGITUDCIVPK_CALZADA
0-74.0909244.6938071A00064027544846602017/06/12 00:00:00+002017AV AVENIDA BOYACA-CL 79 02SOLO DANOSCHOQUEENGATIVA2017/06/12 05:30:00+004.693807-74.09092410006772.0221236.0
1-74.1210004.6030002A001233353105334992020/11/19 00:00:00+002020CL 26 S- KR 50 02CON HERIDOSOTROPUENTE ARANDA2020/11/19 02:05:00+004.603000-74.12100016004560.0NaN
2-74.0420004.6820004A001232786105336292020/11/10 00:00:00+002020KR 9 - CL 100 02SOLO DANOSCHOQUEUSAQUEN2020/11/10 13:30:00+004.682000-74.04200030001107.0NaN
3-74.1669374.5871877A00020070544126992015/05/11 00:00:00+002015CL 63A-KR 72 S 02SOLO DANOSCHOQUECIUDAD BOLIVAR2015/05/11 10:50:00+004.587187-74.16693719001483.0136166.0
4-74.0929014.6076488A00040286244478452016/06/08 00:00:00+002016KR 27-CL 9 14SOLO DANOSCHOQUELOS MARTIRES2016/06/08 21:30:00+004.607648-74.09290114000548.0239719.0
5-74.0420004.7780009A001179874105335872020/08/03 00:00:00+002020AU NORTE - CL 200 02CON MUERTOSATROPELLOSUBA2020/08/03 14:05:00+004.778000-74.0420001006455.0NaN
6-74.0558534.72462610A00024010544248832015/09/26 00:00:00+002015KR 52A-CL 137A 35SOLO DANOSCHOQUESUBA2015/09/26 18:00:00+004.724626-74.05585311008301.031431.0
7-74.0390004.79600012A001233064105335032020/11/23 00:00:00+002020AU NORTE - CL 220 02SOLO DANOSOTROUSAQUEN2020/11/23 11:50:00+004.796000-74.039000NaNNaN
8-74.1105854.69357813A00055101044687082016/12/27 00:00:00+002016CL 69A-KR 89A 02CON HERIDOSCHOQUEENGATIVA2016/12/27 19:00:00+004.693578-74.11058510006813.0219627.0
9-74.1357664.65933015A00068649544955192017/10/02 00:00:00+002017AV AVENIDA CIUDAD DE CALI-KR 17 2CON HERIDOSCHOQUEFONTIBON2017/10/02 09:20:00+004.659330-74.13576650008531.0272348.0

Last rows

XYOBJECTIDFORMULARIOCODIGO_ACCIDENTEFECHA_OCURRENCIA_ACCANO_OCURRENCIA_ACCDIRECCIONGRAVEDADCLASE_ACCLOCALIDADFECHA_HORA_ACCLATITUDLONGITUDCIVPK_CALZADA
199136-74.1710004.628000421902A001298808105401392021/05/16 00:00:00+002021KR 86 - AV AVENIDA CIUDAD DE VILLAVICENCIO 02SOLO DANOSCHOQUEKENNEDY2021/05/16 23:30:00+004.628000-74.1710008005072.0NaN
199137-74.1378644.626886421903A001301685105425122021/06/20 00:00:00+002021AV AVENIDA BOYACA - CL 3 B 18CON HERIDOSCHOQUEKENNEDY2021/06/20 06:15:00+004.626886-74.1378648005344.0NaN
199138-74.1405874.615627421904A001241254105343702021/02/28 00:00:00+002021KR 72 - CL 35 S 79CON HERIDOSVOLCAMIENTOKENNEDY2021/02/28 01:50:00+004.615627-74.1405878007974.0NaN
199139-74.1580004.628000421905A001339673105471442021/08/14 00:00:00+002021KR 80 - CL 38 S 02SOLO DANOSCHOQUEKENNEDY2021/08/14 15:53:00+004.628000-74.15800050006497.0NaN
199140-74.1410004.610000421906A001340014105473982021/08/20 00:00:00+002021KR 72 - CL 38 S 02SOLO DANOSCHOQUEKENNEDY2021/08/20 01:15:00+004.610000-74.1410008009540.0NaN
199141-74.1600004.637000421907A001341297105485222021/08/30 00:00:00+002021KR 86 F - CL 33 S 02SOLO DANOSCHOQUEKENNEDY2021/08/30 16:31:00+004.637000-74.1600008003090.0NaN
199142-74.1670004.628000421908A001305748105461162021/08/03 00:00:00+002021CL 42 B S- KR 81 L 02CON HERIDOSATROPELLOKENNEDY2021/08/03 14:00:00+004.628000-74.1670008005066.0NaN
199143-74.1582474.624830421909A001238302105360742021/03/19 00:00:00+002021DG 2 S- KR 79 12CON HERIDOSCHOQUEKENNEDY2021/03/19 12:50:00+004.624830-74.1582478005839.0NaN
199144-74.1670004.622000421910A001297106105381812021/04/18 00:00:00+002021CL 43 S- KR 80 02CON HERIDOSCHOQUEKENNEDY2021/04/18 21:21:00+004.622000-74.1670008011660.0NaN
199145-74.1680004.630000421911A001304271105442262021/07/12 00:00:00+002021AV AVENIDA CIUDAD DE CALI - CL 42 S 02CON HERIDOSCAIDA DE OCUPANTEKENNEDY2021/07/12 19:53:00+004.630000-74.1680008004623.0NaN